{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import argparse\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import cv2\n",
    "from tqdm import tqdm\n",
    "import matplotlib.pyplot as plt \n",
    "\n",
    "import torch\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from torchvision import transforms, utils\n",
    "import torch.backends.cudnn as cudnn\n",
    "\n",
    "from pytvision.transforms.aumentation import  ObjectImageMetadataTransform\n",
    "from pytvision.transforms import transforms as mtrans\n",
    "\n",
    "sys.path.append('../')\n",
    "import torch.nn.functional as TF\n",
    "from torchlib.transforms import functional as F\n",
    "from torchlib.datasets.factory  import FactoryDataset\n",
    "from torchlib.datasets.datasets import Dataset\n",
    "from torchlib.datasets.fersynthetic  import SyntheticFaceDataset\n",
    "\n",
    "from torchlib.attentionnet import AttentionGMMNeuralNet\n",
    "from aug import get_transforms_aug, get_transforms_det\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CONFIGURATE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "attmap.png\n",
      "att.png\n",
      "att_snt.png\n",
      "cratedataset.txt\n",
      "experiments_cls.csv\n",
      "experiments_recovery.csv\n",
      "feratt_atentionresnet34_attgmm_adam_affectnetdark_dim64_preactresnet18x32_fold0_000\n",
      "feratt_atentionresnet34_attgmm_adam_affectnetdark_dim64_preactresnet18x32_fold0_001\n",
      "feratt_atentionresnet34_attgmm_adam_affectnetdark_dim64_preactresnet18x32_fold0_002\n",
      "feratt_atentionresnet34_attgmm_adam_bu3dfe_dim64_preactresnet18x32_fold0_000\n",
      "feratt_atentionresnet34_attgmm_adam_bu3dfe_dim64_preactresnet18x64_fold0_000\n",
      "feratt_atentionresnet34_attgmm_adam_ferblack_dim64_preactresnet18x32_fold0_000\n",
      "feratt_attgmmnet_ferattentiongmm_attloss_adam_affectnetdark_dim1000_preactresnet18x32_fold0_003\n",
      "feratt_attgmmnet_ferattentiongmm_attloss_adam_affectnetdark_dim1000_preactresnet18x32_fold0_010\n",
      "feratt_attgmmnet_ferattentiongmm_attloss_adam_affectnetdark_dim64_preactresnet18x32_fold0_009\n",
      "feratt_attgmmnet_ferattentiongmm_attloss_adam_bu3dfedark_dim64_preactresnet18x32_fold0_009\n",
      "feratt_attgmmnet_ferattentiongmm_attloss_adam_ckdark_dim1000_preactresnet18x32_fold0_004\n",
      "feratt_attgmmnet_ferattentiongmm_attloss_adam_ckdark_dim1000_preactresnet18x32_fold0_005\n",
      "feratt_attgmmnet_ferattentiongmm_attloss_adam_ckdark_dim64_preactresnet18x32_fold0_005\n",
      "feratt_attgmmnet_ferattentiongmm_attloss_adam_ckdark_dim64_preactresnet18x32_fold0_006\n",
      "feratt_attgmmnet_ferattentiongmm_attloss_adam_ckdark_dim64_preactresnet18x32_fold0_007\n",
      "feratt_attgmmnet_ferattentiongmm_attloss_adam_ckdark_dim64_preactresnet18x32_fold0_008\n",
      "feratt_attgmmnet_ferattentiongmm_attloss_adam_ckdark_dim64_preactresnet18x32_fold0_009\n",
      "feratt_attnet_ferattention_attgmm_adam_affectnetdark_dim64_preactresnet18x32_fold0_003\n",
      "feratt_attnet_ferattention_attloss_adam_affectnetdark_dim64_preactresnet18x32_fold0_003\n",
      "ferattpaper_attgmmnet_ferattentiongmm_attloss_adam_bu3dfedark_dim64_preactresnet18x32_fold0_000\n",
      "ferattpaper_attgmmnet_ferattentiongmm_attloss_adam_bu3dfedark_dim64_preactresnet18x32_fold0_004\n",
      "ferattpaper_attgmmnet_ferattentiongmm_attloss_adam_bu3dfedark_dim64_preactresnet18x32_fold1_005\n",
      "ferattpaper_attgmmnet_ferattentiongmm_attloss_adam_bu3dfedark_dim64_preactresnet18x32_fold2_006\n",
      "ferattpaper_attgmmnet_ferattentiongmm_attloss_adam_ckdark_dim1000_preactresnet18x32_fold0_000\n",
      "ferattpaper_attgmmnet_ferattentiongmm_attloss_adam_ckdark_dim64_preactresnet18x32_fold0_000\n",
      "ferattpaper_attgmmnet_ferattentiongmm_attloss_adam_ckdark_dim64_preactresnet18x32_fold1_001\n",
      "ferattpaper_attgmmnet_ferattentiongmm_attloss_adam_ckdark_dim64_preactresnet18x32_fold2_002\n",
      "ferattpaper_attgmmnet_ferattentiongmm_attloss_adam_ckdark_dim64_preactresnet18x32_fold3_003\n",
      "ferattpaper_attgmmnet_ferattentiongmm_attloss_adam_ckdark_dim64_preactresnet18x32_fold4_007\n",
      "ferattpaperstn_attgmmnet_ferattentiongmm_attloss_adam_affectnetdark_dim64_preactresnet18x32_fold0_001\n",
      "ferattpaperstn_attgmmnet_ferattentiongmm_attloss_adam_affectnetdark_dim64_preactresnet18x32_fold4_000\n",
      "ferattpaperstn_attgmmstnnet_ferattentionstn_attloss_adam_affectnetdark_dim64_preactresnet18x128_fold0_003\n",
      "ferattpaperstn_attgmmstnnet_ferattentionstn_attloss_adam_affectnetdark_dim64_preactresnet18x32_fold0_002\n",
      "ferattpaperstn_ferattentionstn_ferattentionstn_attloss_adam_affectnetdark_dim64_preactresnet18x32_fold0_002\n",
      "image.png\n",
      "recover_ck.png\n",
      "recover.png\n",
      "result\n",
      "srf.png\n",
      "videos\n"
     ]
    }
   ],
   "source": [
    "!ls ../out/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "project          = '../out'\n",
    "name             = 'ferattpaperstn_attgmmnet_ferattentiongmm_attloss_adam_affectnetdark_dim64_preactresnet18x32_fold0_001'\n",
    "pathnamedataset  = '~/.datasets'\n",
    "pathmodel        = os.path.join( project, name, 'models/model_best.pth.tar' ) #model_best\n",
    "pathproject      = os.path.join( project, name )\n",
    "batch_size       = 1\n",
    "workers          = 1\n",
    "cuda             = False\n",
    "parallel         = False\n",
    "gpu              = 1\n",
    "seed             = 1\n",
    "imsize           = 128"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LOAD MODEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Setting up a new session...\n",
      "WARNING:visdom:Without the incoming socket you cannot receive events from the server or register event handlers to your Visdom client.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> Load model ...\n",
      "=> loading checkpoint '../out/ferattpaperstn_attgmmnet_ferattentiongmm_attloss_adam_affectnetdark_dim64_preactresnet18x32_fold0_001/models/model_best.pth.tar'\n",
      "=> loaded checkpoint for ferattentiongmm arch!\n"
     ]
    }
   ],
   "source": [
    "# load model\n",
    "print('>> Load model ...')\n",
    "\n",
    "net = AttentionGMMNeuralNet( \n",
    "    patchproject=project, \n",
    "    nameproject=name, \n",
    "    no_cuda=cuda, \n",
    "    parallel=parallel, \n",
    "    seed=seed, \n",
    "    gpu=gpu \n",
    "    )\n",
    "\n",
    "if net.load( pathmodel ) is not True:\n",
    "    assert(False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DATASETS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> Load dataset ...\n",
      "['Neutral - NE', 'Happiness - HA', 'Surprise - SU', 'Sadness - SA', 'Anger - AN', 'Disgust - DI', 'Fear - FR', 'Contempt - CO']\n",
      "8\n"
     ]
    }
   ],
   "source": [
    "print('>> Load dataset ...')\n",
    "namedataset = FactoryDataset.affect\n",
    "subset = FactoryDataset.validation\n",
    "imagesize=128\n",
    "\n",
    "dataset = Dataset(    \n",
    "    data=FactoryDataset.factory(\n",
    "        pathname=pathnamedataset, \n",
    "        name=namedataset, \n",
    "        subset=subset, \n",
    "        download=True \n",
    "    ),\n",
    "    num_channels=3,\n",
    "    transform=transforms.Compose([\n",
    "            mtrans.ToResize( (imagesize,imagesize), resize_mode='square' ),\n",
    "            mtrans.ToTensor(),\n",
    "            #mtrans.ToMeanNormalization( mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225] )\n",
    "            mtrans.ToNormalization(),\n",
    "            ])\n",
    "    )\n",
    "\n",
    "# emotions = dataset.data.classes\n",
    "emotions = ['Neutral - NE', 'Happiness - HA', 'Surprise - SU', 'Sadness - SA', 'Anger - AN', 'Disgust - DI', 'Fear - FR', 'Contempt - CO']\n",
    "\n",
    "# if namedataset == FactoryDataset.bu3dfe:\n",
    "#     emotions = emotions[:-1]\n",
    "print(emotions)\n",
    "print(len(emotions))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(128, 128, 3)\n",
      "tensor([[0.1326, 0.1045, 0.2028, 0.0349, 0.1006, 0.1573, 0.0165, 0.2508]],\n",
      "       device='cuda:1')\n",
      "tensor(7, device='cuda:1')\n",
      "Contempt - CO\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import random\n",
    "\n",
    "def sigmoid(x):\n",
    "    return 1. / (1 + np.exp(-x))\n",
    "\n",
    "def norm(x):\n",
    "    x = x-x.min()\n",
    "    x = x / x.max()\n",
    "    return x\n",
    "\n",
    "def mean_normalization(image, mean, std):\n",
    "    tensor = image.float()/255.0\n",
    "    result_tensor = []\n",
    "    for t, m, s in zip(tensor, mean, std):  \n",
    "        result_tensor.append(t.sub_(m).div_(s))            \n",
    "    return torch.stack(result_tensor, 0)\n",
    "\n",
    "def pad(image, xypad):\n",
    "    h,w = image.shape\n",
    "    im_pad = np.zeros( (h+2*xypad,w+2*xypad) )\n",
    "    im_pad[xypad:xypad+h,xypad:xypad+w] = image\n",
    "    return im_pad\n",
    "    \n",
    "def crop(image, xycrop):\n",
    "    h,w = image.shape[:2]\n",
    "    image = image[ xycrop:h-xycrop,xycrop:w-xycrop ]\n",
    "    return image \n",
    "    \n",
    "imagesize=128\n",
    "image = cv2.imread('../rec/selfie_happy_dos.png')[:,:,0]\n",
    "# image = pad(image,50)\n",
    "# image = crop(image,50)\n",
    "\n",
    "# sigma=0.1\n",
    "# image = image/255.0\n",
    "# noise = np.array([random.gauss(0,sigma) for i in range( image.shape[0]*image.shape[1]  )])\n",
    "# noise = noise.reshape(image.shape[0],image.shape[1])\n",
    "# image = (np.clip(image+noise,0,1)*255).astype(np.uint8)\n",
    "\n",
    "image = np.stack( (image,image,image), axis=2 )\n",
    "image = cv2.resize( image, (imagesize, imagesize) )\n",
    "\n",
    "# gamma=0.1\n",
    "# image[:,:,0] = norm((image[:,:,0]/255)**gamma)*255\n",
    "# image[:,:,1] = norm((image[:,:,1]/255)**gamma)*255\n",
    "# image[:,:,2] = norm((image[:,:,2]/255)**gamma)*255\n",
    "\n",
    "image = torch.from_numpy(image).permute( (2,0,1) ).unsqueeze(0).float()\n",
    "# image = mean_normalization(image, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
    "image = image/255\n",
    "\n",
    "z, y_lab_hat, att, fmap, srf = net( image )\n",
    "\n",
    "\n",
    "att  = att.data.cpu().numpy().transpose(2,3,1,0)[...,0]\n",
    "fmap = fmap.data.cpu().numpy().transpose(2,3,1,0)[:,:,0,0]\n",
    "srf  = srf.data.cpu().numpy().transpose(2,3,1,0)[...,0]\n",
    "image = image.data.cpu().numpy().transpose(2,3,1,0)[...,0]\n",
    "y_lab_hat_max = y_lab_hat.argmax()\n",
    "\n",
    "\n",
    "print(image.shape)\n",
    "print(y_lab_hat)\n",
    "print(y_lab_hat_max)\n",
    "print(emotions[y_lab_hat_max])\n",
    "\n",
    "plt.figure( figsize=(16,8))\n",
    "plt.subplot(141)\n",
    "plt.imshow( norm(image) )\n",
    "plt.title('image')   \n",
    "plt.axis('off')\n",
    "plt.subplot(142)\n",
    "plt.imshow( (fmap))\n",
    "plt.title('attention map') \n",
    "plt.axis('off' )\n",
    "plt.subplot(143)\n",
    "plt.imshow( srf.sum(2) )\n",
    "plt.title('feature map') \n",
    "plt.axis('off' )\n",
    "plt.subplot(144)\n",
    "plt.imshow( norm(att) )  \n",
    "# plt.title('class {}'.format( y_lab_hat_max ) ) \n",
    "plt.title('attention feature') \n",
    "plt.axis('off')\n",
    "\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.misc\n",
    "import random\n",
    "\n",
    "def sigmoid(x):\n",
    "    return 1. / (1 + np.exp(-x))\n",
    "\n",
    "def norm(x):\n",
    "    x = x-x.min()\n",
    "    x = x / x.max()\n",
    "    return x\n",
    "\n",
    "def mean_normalization(image, mean, std):\n",
    "    tensor = image.float()/255.0\n",
    "    result_tensor = []\n",
    "    for t, m, s in zip(tensor, mean, std):  \n",
    "        result_tensor.append(t.sub_(m).div_(s))            \n",
    "    return torch.stack(result_tensor, 0)\n",
    "\n",
    "def pad(image, xypad):\n",
    "    h,w = image.shape[:2]\n",
    "    im_pad = np.zeros( (h+2*xypad,w+2*xypad,3) )\n",
    "    im_pad[xypad:xypad+h,xypad:xypad+w, :] = image\n",
    "    return im_pad\n",
    "\n",
    "def crop(image, xycrop):\n",
    "    h,w = image.shape[:2]\n",
    "    image = image[ xycrop:h-xycrop,xycrop:w-xycrop , :]\n",
    "    return image \n",
    "\n",
    "\n",
    "def fusion( imx, imy, x=0,y=0, alpha=0.5 ):\n",
    "    n,m = imy.shape[:2]\n",
    "    imx[ x:x+n,y:y+m, : ] = alpha*imx[ x:x+n,y:y+m, : ] + (1-alpha)*imy\n",
    "    return imx\n",
    "\n",
    "def noise(image, sigma=0.05):\n",
    "    image = image/255.0\n",
    "    noise = np.array([random.gauss(0,sigma) for i in range( image.shape[0]*image.shape[1]*3  )])\n",
    "    noise = noise.reshape(image.shape[0],image.shape[1],3)\n",
    "    image = (np.clip(image+noise,0,1)*255).astype(np.uint8)\n",
    "    return image\n",
    "    \n",
    "def ligth(image, gamma=0.2):\n",
    "    image[:,:,0] = norm((image[:,:,0]/255)**gamma)*255\n",
    "    image[:,:,1] = norm((image[:,:,1]/255)**gamma)*255\n",
    "    image[:,:,2] = norm((image[:,:,2]/255)**gamma)*255\n",
    "    return image\n",
    "\n",
    "class cTrack(object):\n",
    "    '''track frame\n",
    "    '''\n",
    "    \n",
    "    def __init__(self, net, image_size=128):\n",
    "        self.imagesize=image_size\n",
    "        self.net=net\n",
    "    \n",
    "    def __call__(self, frame):\n",
    "        \n",
    "        #image = frame\n",
    "        image = frame.mean(axis=2)        \n",
    "        image = np.stack( (image,image,image), axis=2 )\n",
    "        \n",
    "        image = cv2.resize( image, (self.imagesize, self.imagesize) )\n",
    "        image = torch.from_numpy(image).permute( (2,0,1) ).unsqueeze(0).float()\n",
    "        #image = mean_normalization(image, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
    "        image = image/255\n",
    "        \n",
    "        zhat, y_lab_hat, att, fmap, srf = self.net( image )\n",
    "        \n",
    "        att = att.data.cpu().numpy().transpose(2,3,1,0)[...,0]\n",
    "        fmap = fmap.data.cpu().numpy().transpose(2,3,1,0)[:,:,0,0]\n",
    "        srf  = srf.data.cpu().numpy().transpose(2,3,1,0)[...,0]\n",
    "        y_lab_hat_max = y_lab_hat.argmax()\n",
    "        \n",
    "        return att, fmap, srf, zhat, y_lab_hat, y_lab_hat_max\n",
    "\n",
    "    \n",
    "class cFrame(object):\n",
    "    '''frames porcess\n",
    "    '''        \n",
    "    def __init__(self, image_size=[640, 640, 3], border=0, offsetx=0, offsety=0):     \n",
    "        self.imagesize  = image_size\n",
    "        self.asp = float(image_size[1])/image_size[0]\n",
    "        self.border = border\n",
    "        self.offsetx = offsetx\n",
    "        self.offsety = offsety \n",
    "       \n",
    "        \n",
    "    def __call__(self, frame):\n",
    "        '''process frame \n",
    "        '''        \n",
    "        #H, W original image size\n",
    "        H=frame.shape[0]; W=frame.shape[1];\n",
    "        \n",
    "        #image canonization\n",
    "        if H>W: frame = frame.transpose(1,0,2)\n",
    "        H=frame.shape[0]; W=frame.shape[1]     \n",
    "        \n",
    "        H1 = int(H - self.border)\n",
    "        W1 = int(H1 * self.asp)\n",
    "        offsetx=self.offsetx\n",
    "        offsety=self.offsety\n",
    "        Wdif = int(np.abs(W - W1) / 2.0)\n",
    "        Hdif = int(np.abs(H - H1) / 2.0)\n",
    "        vbox = np.array([[Wdif, Hdif], [W - Wdif, H - Hdif]])\n",
    "\n",
    "        frame_p = frame[vbox[0, 1]+offsety:vbox[1, 1]+offsety, vbox[0, 0]+offsetx:vbox[1, 0]+offsetx, : ]; #(2, 1, 0)\n",
    "        aspY = float(self.imagesize[0]) / frame_p.shape[0]\n",
    "        aspX = float(self.imagesize[1]) / frame_p.shape[1]\n",
    "\n",
    "        frame_p = scipy.misc.imresize(frame_p, (self.imagesize[0], self.imagesize[1]), interp='bilinear')\n",
    "        \n",
    "        return frame_p\n",
    "    \n",
    "    \n",
    "def drawcaption( y, emotions, imsizeout=(200,200) ):    \n",
    "    \n",
    "    ne = len(emotions)\n",
    "    colors = ([150,150,150],[125,125,125],[255,255,255],[255,255,255])\n",
    "    hbox=40; wbox= 135 + 170\n",
    "    \n",
    "    imsize=(hbox*ne,wbox,3)\n",
    "    imemotions = np.zeros( imsize, dtype=np.uint8 )*255    \n",
    "    \n",
    "    ymax = y.argmax()\n",
    "    \n",
    "    for i, yi in enumerate(y):\n",
    "\n",
    "        k  = 1 if y[i]>0.5 else 0\n",
    "        kh = 1 if ymax==i else 0\n",
    "    \n",
    "        bbox = np.array([[0,0],[wbox,0],[wbox,hbox],[0,hbox]]);    \n",
    "        bbox[:,0] += 0\n",
    "        bbox[:,1] += 26-26 + (i)*40\n",
    "        imemotions = cv2.fillConvexPoly(imemotions, bbox, color=colors[kh] )\n",
    "        \n",
    "        bbox = np.array([[0,0],[int(wbox*y[i]),0],[int(y[i]*wbox),hbox],[0,hbox]]);    \n",
    "        bbox[:,0] += 0\n",
    "        bbox[:,1] += 26-26 + (i)*40\n",
    "        imemotions = cv2.fillConvexPoly(imemotions, bbox, color=[255,160,122] )\n",
    "        \n",
    "        cv2.putText(\n",
    "                imemotions, \n",
    "                #'{}: {:.3f}'.format(emotions[i][:-5],y[i]),\n",
    "                '{}: {:.2f}%'.format(emotions[i][:-5], y[i]*100 ),\n",
    "                (2, 26 + (i)*40), \n",
    "                color=colors[2+kh], \n",
    "                fontFace=cv2.FONT_HERSHEY_SIMPLEX, \n",
    "                fontScale=1, \n",
    "                thickness=2\n",
    "                )\n",
    "        \n",
    "    #imemotions = imemotions[20:-20,20:-20,:]\n",
    "    imemotions = cv2.resize( imemotions, imsizeout )\n",
    "    return imemotions\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "DONE!!!\n"
     ]
    }
   ],
   "source": [
    "import cv2 \n",
    "from IPython.display import clear_output\n",
    "\n",
    "filename = '../out/videos/sad2.avi'  \n",
    "cap = cv2.VideoCapture( filename )\n",
    "\n",
    "print(cap.isOpened())\n",
    "frame_proc = cFrame( image_size=[128,128,3] ) \n",
    "track   = cTrack( net, image_size=128 )\n",
    "\n",
    "k = 0\n",
    "iframe=0\n",
    "totalframe=30\n",
    "iniframe=0 #0, 1700\n",
    "\n",
    "bligth=False\n",
    "mingam, maxgam = 0.5, 3.0 \n",
    "#gammas = mingam + np.random.rand(totalframe)*( maxgam - mingam )\n",
    "gammas = np.linspace( mingam, maxgam, num=totalframe+1 )\n",
    "gammas = gammas[::-1]\n",
    "#gammas.sort()\n",
    "\n",
    "\n",
    "bnoise=False\n",
    "minnoise, maxnoise = 0.01, 0.08\n",
    "# sigmas = minnoise + np.random.rand(totalframe)*( maxnoise - minnoise )\n",
    "sigmas = np.linspace( minnoise, maxnoise, num=totalframe+1 )\n",
    "sigmas = sigmas[::-1]\n",
    "#sigmas.sort()\n",
    "\n",
    "\n",
    "# for every frame\n",
    "while(cap.isOpened()):\n",
    "    \n",
    "    # read\n",
    "    #for i in range(100): \n",
    "    #    ret, frame = cap.read()\n",
    "\n",
    "    ret, frame = cap.read()\n",
    "    if not ret:\n",
    "        k+=1\n",
    "        print('Error video')\n",
    "        break\n",
    "\n",
    "    if k%1 != 0 or k < iniframe:\n",
    "        k+=1\n",
    "        continue\n",
    "        \n",
    "    #print(k)   \n",
    "    #frame = frame[:-300,850:,:]\n",
    "    #frame = frame[50:-350,500:1350,:]\n",
    "    \n",
    "    #frame = frame[0:500,0:500,:] # happy, sad1\n",
    "    #frame = frame[0:700,0:700,:]\n",
    "    #frame = frame[100:700,100:700,:]\n",
    "    frame = frame[0:320,350:720,:] # sad2(crop 30)\n",
    "    #frame = frame[50:450,300:720,:] #fear\n",
    "    \n",
    "    image = frame_proc( frame )\n",
    "    image = crop(image,30)\n",
    "    #image = pad(image, 20)\n",
    "    #image = ligth(image, gamma = 0.3 )\n",
    "       \n",
    "    #noise\n",
    "    if bnoise:\n",
    "        image = noise(image, sigma=sigmas[iframe - iniframe])  \n",
    "        image = np.clip(image, 0, 255 )\n",
    "    \n",
    "    #ligth\n",
    "    if bligth:\n",
    "        image = ligth(image, gamma = gammas[iframe - iniframe] )\n",
    "        image = np.clip(image, 0, 255 )\n",
    "    \n",
    "    att, fmap, srf, zhat, yhat, yhat_max = track( image )\n",
    "    yhat = TF.softmax( yhat, dim=1 )[0,:]\n",
    "    att_map = att.mean(axis=2)\n",
    "    \n",
    "    #print(yhat)\n",
    "    \n",
    "    \n",
    "    #create video frame   \n",
    "    imsize=500\n",
    "    midsize=250\n",
    "    layer   = np.zeros( [768, 2024, 3] , dtype=np.uint8 )*255 #1024\n",
    "    \n",
    "    caption = drawcaption(yhat, emotions )\n",
    "    image   = cv2.resize( image, (imsize,imsize) )[:,:,(2,1,0)]\n",
    "    att     = cv2.resize( norm(att)*255, (imsize,imsize) )\n",
    "    fmap    = cv2.resize( norm(fmap)*255, (imsize,imsize) )\n",
    "    srf     = cv2.resize( norm(srf.sum(axis=2))*255, (imsize,imsize) )\n",
    "    \n",
    "    #att     = cv2.resize( norm(att)*255, (midsize,midsize) )\n",
    "    #fmap    = cv2.resize( norm(fmap)*255, (midsize,midsize) )\n",
    "    #srf     = cv2.resize( norm(srf.sum(axis=2))*255, (midsize,midsize) )\n",
    "    \n",
    "    #https://www.learnopencv.com/applycolormap-for-pseudocoloring-in-opencv-c-python/\n",
    "    fmap = cv2.applyColorMap( fmap.astype(np.uint8) , cv2.COLORMAP_JET)[:,:,(2,1,0)]\n",
    "    srf = cv2.applyColorMap( srf.astype(np.uint8), cv2.COLORMAP_JET)[:,:,(2,1,0)]\n",
    "    \n",
    "    #att     = np.concatenate( (att, att_t), axis=0 )\n",
    "    #feature = np.concatenate( (fmap, srf ), axis=0 )\n",
    "    #feature = np.stack( (feature, feature,feature ), axis=2 )    \n",
    "    #image   = np.concatenate( (image, feature, att), axis=1 )\n",
    "    \n",
    "    image   = np.concatenate( (image, fmap, srf, att), axis=1 )\n",
    "    \n",
    "    \n",
    "    #layer\n",
    "    \n",
    "    layer = fusion(layer, image, x=10+100, y=10, alpha=0.0 )\n",
    "    layer = fusion(layer, caption, x=20+100, y=20, alpha=0.2 )\n",
    "    \n",
    "    cv2.imwrite('../out/result/{:06d}.png'.format( iframe + 0*(totalframe + 1) ), layer[:,:,(2,1,0)] )\n",
    "    k+=1; iframe+=1\n",
    "    \n",
    "    # show\n",
    "    ishow=False\n",
    "    if ishow:\n",
    "        plt.figure( figsize=(25,8) )\n",
    "        plt.imshow( layer )\n",
    "        plt.axis('off')\n",
    "        plt.show()\n",
    "        clear_output(wait=True) \n",
    "    \n",
    "    if iframe%100 == 0:\n",
    "        print(k, iframe)        \n",
    "    \n",
    "    if k > iniframe+totalframe:\n",
    "        break\n",
    "\n",
    "    \n",
    "        \n",
    "        \n",
    "cap.release()\n",
    "print('DONE!!!')\n",
    "\n"
   ]
  },
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